Image Segmentation Research Based On Particle Swarm Optimization Algorithm

Image segmentation is one of the research hotspots in the field of both image processing and computer vision, also an essential base for pattern recognition. The technology of image segmentation determines the ultimate results and quality of image analysis and interpretation. Only through thorough and precise image segmentation can the higher-level image analysis and interpretation be made possible. In essence, the image segmentation is just a problem about searching optimal segmentation parameter in the complex parameter space. Various intelligent optimization algorithms are able to perform rapid and effective computation for the complex nonlinear multi-dimensional data space, and lead to not only global optimal solution, but also far less computation time.Threshold segmentation and clustering segmentation are the most frequently and extensively used methods among numerous image segmentation algorithms. Intelligen optimization algorithms are applied to image segmentation in two aspects:Selection of the optimal threshold and feature space clustering. In selection of the optimal threshold, intelligent algorithms are used as the optimization tool, compute the optimum of objective function by iterative method according to some criteria and further obtain the optimal threshold for segmented images. Feature space clustering means combining intelligent optimization algorithms with clustering image segmentation technology to avoid local optimum and simultaneously obtain the optimal clustering as soon as possible. We focus mainly on the image segmentation algorithm based on particle swarm optimization (PSO) and try to accomplish the image segmentation in an automatic, precise and rapid way by establishing segmentation method with self-adaptability and robustness through integrated utilization of traditional and modern techniques with PSO algorithm as the optimizing tool.Our work involves mainly the improvement of PSO optimization, generalization of Otsu and KSW method; establishment of the framework for the threshold segmentation algorithm based on maximum fuzzy entropy and improved particle swarm, improvement of the clustering segmentation algorithm based on fuzzy cluster analysis and PSO, and access to categories of the image automatic clustering based on mutual information and optimal cluster distance measure. There are six aspects as follows:(1) Overview image segmentation algorithms and progress of image segmentation research based on intelligent optimization algorithms; (2) Overview the basic theory, improvement and research progress of bPSO and analyze the advantage of avoiding premature convergence by keeping particles diversified; (3) Outline systematically the basic principle and technology of generalized Otsu method and KSW method;. (4) Study the key technique in the threshold segmentation method based on maximum fuzzy entropy, analyze the fuzzy partition way of fuzzy set by maximum fuzzy entropy method, the definition of fuzzy entropy and problems in solving the optimization and propose the solution; (5) Analyze the advantage and disadvantage of much improved clustering algorithm based on fuzzy C-means as well as the simple effective utilization of image space structure information in new weighted window construction method and its impact on convergence rate and convergence precision of the segmentation; (6) Study a new method for automatically searching the number of cluster categories from monotonicity of maximum intra-category distance and mean deviation in combination with mutual information theory and improved PSO.The main achievement and innovation are summarized as follow. (1) Design a new PSO (sdPSO) algorithm based on symmetric distribution of the particle space with enhanced performance to search optimal. (2) Propose the two-dimensional improved Otsu method and improved KSW method based on neighborhood gray-scale contrast ratio, and the run time is considerably shortened with sdPSO algorithm searching the optimal threshold. (3) Propose a new threshold segmentation algorithm based on three-dimensional maximum fuzzy entropy and sdPSO algorithm, and segmentation result and precision are better for grey-scaled images. (4) Propose two improved FCM algorithms:one is SDFCM based on spatial distance similarity and the other GSDSFCM based on both grey-scale and spatial distance similarity. Algorithm sdPSO is used to search the optimal clustering center. Synthetic performances are all enhanced, such as the segmentation accuracy, anti-noise capacity and segmentation time. (5) Propose a new image clustering segmentation algorithm (MIM-DIS-PSO) taking sdPSO as optimization technology and using the mutual information and intra-category distance measure as the optimization object. The images after segmentation have such merits as accurate target information, complete interior feature as well as continuous and clear edges. Moreover, all quality evaluation parameters for images after segmentation are good. (6) Propose an image clustering segmentation algorithm based on monotonicity of maximum intra-category distance and mean deviation. The algorithm is able to automatically get the optimal number of clustering categories for the image, overcome the trend for too small optimal clustering number or only directing to the maximum clustering number, avoiding the clustering repetition usually occurring for some automatic clustering segmentation at the possible category number interval. Therefore, the optimal number clustering categories obtained is more reasonable.